Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probably COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical accounts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	17,114
2	New Jersey	15,045
3	Massachusetts	10,695
4	Rhode Island	9,939
5	Connecticut	8,914
6	District of Columbia	8,011
7	Louisiana	6,593
8	Delaware	6,099
9	Illinois	5,587
10	Maryland	4,875
11	Michigan	4,570
12	Pennsylvania	4,385
13	Nebraska	3,716
14	Iowa	3,505
15	Indiana	3,407
16	South Dakota	3,283
17	Colorado	3,171
18	Mississippi	2,918
19	Georgia	2,874
20	Virginia	2,527
21	Washington	2,276
22	Kansas	2,142
23	New Mexico	2,142
24	New Hampshire	2,090
25	Tennessee	2,074
26	Nevada	1,911
27	Ohio	1,893
28	Alabama	1,844
29	Florida	1,807
30	North Dakota	1,799
31	Utah	1,785
32	Minnesota	1,660
33	Wisconsin	1,582
34	California	1,581
35	Missouri	1,533
36	Vermont	1,467
37	South Carolina	1,387
38	Kentucky	1,381
39	Arizona	1,366
40	North Carolina	1,280
41	Texas	1,265
42	Arkansas	1,224
43	Idaho	1,218
44	Wyoming	1,097
45	Oklahoma	1,094
46	Maine	989
47	West Virginia	723
48	Oregon	708
49	Puerto Rico	635
50	Alaska	509
51	Hawaii	437
52	Montana	426

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	276
2	District of Columbia	228
3	Massachusetts	224
4	Delaware	222
5	New Jersey	201
6	Illinois	183
7	Nebraska	183
8	Connecticut	169
9	Maryland	163
10	New York	146
11	Iowa	143
12	Minnesota	125
13	Indiana	103
14	Kansas	97
15	Mississippi	90
16	South Dakota	89
17	Colorado	84
18	Pennsylvania	82
19	Virginia	81
20	New Mexico	73
21	Louisiana	70
22	Georgia	68
23	Alabama	63
24	North Dakota	63
25	New Hampshire	62
26	Washington	62
27	Kentucky	59
28	Michigan	57
29	Wisconsin	56
30	California	51
31	North Carolina	49
32	Ohio	47
33	Arizona	46
34	Utah	41
35	Tennessee	40
36	Texas	40
37	Missouri	34
38	Maine	30
39	Florida	29
40	Nevada	25
41	Arkansas	24
42	Oklahoma	24
43	South Carolina	24
44	Wyoming	22
45	Oregon	18
46	Puerto Rico	16
47	Idaho	13
48	West Virginia	13
49	Vermont	7
50	Alaska	2
51	Hawaii	1
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,347
2	New Jersey	990
3	Connecticut	784
4	Massachusetts	660
5	Louisiana	459
6	Michigan	434
7	District of Columbia	403
8	Rhode Island	366
9	Pennsylvania	281
10	Maryland	248
11	Illinois	247
12	Indiana	210
13	Delaware	207
14	Colorado	163
15	Mississippi	133
16	Georgia	125
17	Washington	118
18	Ohio	108
19	Nevada	95
20	Minnesota	90
21	Virginia	90
22	Vermont	86
23	New Hampshire	83
24	New Mexico	82
25	Alabama	75
26	Florida	74
27	Iowa	73
28	Missouri	73
29	Kentucky	67
30	Oklahoma	65
31	California	64
32	Wisconsin	64
33	Arizona	61
34	South Carolina	61
35	Kansas	56
36	North Carolina	49
37	Nebraska	47
38	Maine	46
39	North Dakota	40
40	Idaho	37
41	Tennessee	36
42	South Dakota	35
43	Texas	35
44	Puerto Rico	31
45	Arkansas	29
46	Oregon	28
47	West Virginia	28
48	Utah	19
49	Montana	14
50	Hawaii	12
51	Wyoming	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Jersey	33
2	New York	24
3	Connecticut	22
4	Massachusetts	22
5	Pennsylvania	18
6	Rhode Island	14
7	District of Columbia	12
8	Illinois	12
9	Louisiana	10
10	Maryland	10
11	Mississippi	9
12	Indiana	7
13	Delaware	6
14	Michigan	6
15	New Hampshire	6
16	Ohio	6
17	Colorado	5
18	Alabama	4
19	Arizona	4
20	Iowa	4
21	Minnesota	4
22	Florida	3
23	Georgia	3
24	Missouri	3
25	South Dakota	3
26	Virginia	3
27	California	2
28	Kentucky	2
29	Nebraska	2
30	Nevada	2
31	New Mexico	2
32	North Carolina	2
33	North Dakota	2
34	South Carolina	2
35	Washington	2
36	Kansas	1
37	Maine	1
38	Oklahoma	1
39	Tennessee	1
40	Texas	1
41	Utah	1
42	Wisconsin	1
43	Alaska	0
44	Arkansas	0
45	Hawaii	0
46	Idaho	0
47	Montana	0
48	Oregon	0
49	Puerto Rico	0
50	Vermont	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	120,170	1	99
Lincoln	Arkansas	69,026	2	99
Nobles	Minnesota	53,308	3	99
Dakota	Nebraska	52,232	4	99
Bledsoe	Tennessee	39,963	5	99
Davidson	Tennessee	4,944	226	92
Richland	South Carolina	2,509	546	82
Pierce	Washington	1,802	751	76
Orange	California	994	1224	61
York	South Carolina	801	1443	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	2,951	1	99
Early	Georgia	2,552	2	99
Terrell	Georgia	2,462	3	99
Essex	New Jersey	1,728	4	99
Nassau	New York	1,724	5	99
Richland	South Carolina	125	466	85
Pierce	Washington	63	774	75
Davidson	Tennessee	50	886	71
Orange	California	21	1279	59
York	South Carolina	18	1324	57

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.